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Plenoptic Stitching: A Scalable Method for Reconstructing 3D Interactive Walkthroughs

机译:渗透拼接:一种重建3D交互式演练的可扩展方法

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Interactive walkthrough applications require detailed 3D models to give users a sense of immersion in an environment. Traditionally these models are built using computer-aided design tools to define geometry and material properties. But creating detailed models is time-consuming and it is also difficult to reproduce all geometric and photometric subtleties of real-world scenes. Computer vision attempts to alleviate this problem by extracting geometry and photogrammetry from images of the real-world scenes. However, these models are still limited in the amount of detail they recover. Image-based rendering generates novel views by resampling a set of images of the environment without relying upon an explicit geometric model. Current such techniques limit the size and shape of the environment, and they do not lend themselves to walkthrough applications. In this paper, we define a parameterization of the 4D plenoptic function that is particularly suitable for interactive walkthroughs and define a method for its sampling and reconstructing. Our main contributions are: 1) a parameterization of the 4D plenoptic function that supports walkthrough applications in large, arbitrarily shaped environments; 2) a simple and fast capture process for complex environments; and 3) an automatic algorithm for reconstruction of the plenoptic function.
机译:交互式演练应用需要详细的3D模型,使用户在环境中浸入沉浸感。传统上,这些模型是使用计算机辅助设计工具构建的,以定义几何和材料属性。但是创建详细的模型是耗时的,并且还难以再现现实世界场景的所有几何和光度微微点。计算机视觉试图通过从真实世界的场景的图像中提取几何和摄影测量来缓解这个问题。但是,这些模型仍然有限于它们恢复的细节量。基于图像的渲染通过重新采样环境的一组图像而不依赖于明确的几何模型来生成新颖视图。当前这种技术限制了环境的尺寸和形状,并且它们不会为行走的应用而宽容。在本文中,我们定义了4D全能函数的参数化,特别适用于交互式演练,并定义其采样和重建的方法。我们的主要贡献是:1)4D全能功能的参数化,支持大型任意形状环境的演练应用; 2)复杂环境的简单快速捕获过程; 3)一种自动重建集水功能的算法。

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